International journal of epidemiology最新文献

Background: The impact of migration on HIV risk among non-migrating household members is poorly understood. We measured HIV incidence among non-migrants living in households with and without migrants in Uganda.

Methods: We used four survey rounds of data collected from July 2011 to May 2018 from non-migrant participants aged 15-49 years in the Rakai Community Cohort Study. Non-migrants were individuals with no-migration between surveys or at the prior survey. Household migration was defined as ≥1 household member migrating into or out of the house from another community between surveys (∼18 months). Incident HIV was defined as testing HIV seropositive following a negative result. Incidence rate ratios (IRRs) were estimated using Poisson regression with generalized estimating equations. Analyses were stratified by gender, migration into or out of the household and the relationship between non-migrants and migrants (e.g. spouse, child).

Results: About 11 318 non-migrants (5674 women) were followed for 37 320 person-years. Twenty-eight percent (6059/21 370) of non-migrant person-visits had recent migration into or out of the household, and 240 HIV incident cases were identified. Overall, non-migrants in migrant households were not at greater risk of acquiring HIV than non-migrants in households without any migration. However, men were significantly more likely to acquire HIV if their spouse had recently migrated in [adjusted IRR: 2.12; 95% confidence interval (CI): 1.05-4.27] or out (adjusted IRR: 4.01; 95% CI, 2.16-7.44) compared with men with no spousal migration.

Conclusions: HIV incidence is higher among non-migrant men with migrant spouses. Targeted HIV testing and prevention interventions like pre-exposure prophylaxis could be considered for men with migrant spouses.

Background: Higher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels.

Methods: The prospective China Kadoorie Biobank recruited >512 000 adults aged 30-79 years in 2004-08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12 years of follow-up.

Results: Overall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41-1.58), 1.49 (1.38-1.61), 1.42 (1.31-1.54) and 1.64 (1.58-1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20-1.23), 1.28 (1.26-1.29), 1.31 (1.29-1.33) and 1.14 (1.10-1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses.

Conclusions: Among relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality.

Background: The healthy worker effect may distort the association between exposure and health effects in workers. However, few studies have investigated both the healthy worker hire and survival effects simultaneously, and they are limited to mortality studies in male workers.

Methods: We utilized a data set comprising South Korean diagnostic medical radiation workers registered in the National Dose Registry between 1996 and 2011, and merged it with mortality and cancer incidence data. Standardized mortality ratios (SMRs) and standardized incidence ratios (SIRs) were computed for comparison with the general population. To account for time-varying confounders influenced by prior occupational radiation exposure, we applied g-estimation using structural nested accelerated failure time models and compared the outcomes with those from Weibull regression.

Results: A total of 1831 deaths and 3759 first primary cancer cases were identified among 93 918 workers. Both male (SMR = 0.44; 95% CI: 0.42, 0.46) and female workers (SMR = 0.53; 95% CI: 0.46, 0.60) showed lower mortality rates compared with national rates. In the SIR analysis, male workers exhibited reduced risks of solid cancer whereas female workers had increased risks. The g-estimation-derived hazard ratios (HRs) from radiation exposure exceeded those from Weibull regression estimates for all-cause death (HR = 2.55; 95% CI: 1.97, 3.23) and all-cancer incidence (HR = 1.96; 95% CI: 1.52, 2.55) in male workers whereas female workers showed the opposite results.

Conclusions: Comprehensive consideration of the healthy worker effect by sex is essential for estimating the unbiased impact of occupational exposure on health outcomes, notably in studies focusing on male mortality.

Background: This study aimed to estimate population-level and state-level lead-attributable mortality burdens stratified by socioeconomic status (SES) class in the USA.

Methods: Based on the National Health and Nutrition Examination Survey (NHANES), we constructed individual-level SES scores from income, employment, education and insurance data. We assessed the association between the blood lead levels (BLL) and all-cause mortality by Cox regression in the NHANES cohort (n = 31 311, 4467 deaths). With estimated hazard ratios (HR) and prevalences of medium (2-5 μg/dL) and high (≥ 5 μg/dL) BLL, we computed SES-stratified population-attributable fractions (PAFs) of all-cause mortality from lead exposure across 1999-2019. We additionally conducted a systematic review to estimate the lead-attributable mortality burden at state-level.

Results: The HR for every 2-fold increase in the BLL decreased from 1.23 (1.10-1.38) for the lowest SES class to 1.05 (0.90-1.23) for the highest SES class. Across all SES quintiles, medium BLL exhibited a greater mortality burden. Individuals with lower SES had higher lead-attributable burdens, and such disparities haver persisted over the past two decades. In 2017-19, annually 67 000 (32 000-112 000) deaths in the USA were attributable to lead exposure, with 18 000 (2000-41 000) of these deaths occurring in the lowest SES class. Substantial disparities in the state-level mortality burden attributable to lead exposure were also highlighted.

Conclusions: These findings suggested that disparities in lead-attributable mortality burden persisted within US adults, due to heterogeneities in the effect sizes of lead exposure as well as in the BLL among different SES classes.

Background: Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage.

Methods: The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009-11 (ages 49-63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population.

Results: Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]).

Conclusions: Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

Background: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale.

Development: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency.

Application: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations.

Conclusions: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.